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on career development strategies. We will select highly qualified candidates who wish to pursue a career in CPC. It is our goal that CPC Fellows will develop the knowledge and skills necessary to function
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research to solve difficult biological questions * “own” projects by developing an independent research vision through training * establish expertise in project areas by actively seeking knowledge from
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lab can be found here: https://clinicalnlp.org/. Qualifications and Skills: • A PhD in computer science, information science, informatics, biomedical informatics, or related field. • Strong knowledge
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), or reinforcement learning (RL) post-training. Experience with multi-GPU training and a strong working knowledge of reinforcement learning are also required. Familiarity with standard software development tools
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who wish to pursue a career in CPC. It is our goal that CPC Fellows will develop the knowledge and skills necessary to function independently, and develop and carry out innovative, impactful studies
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mechanistic and structural knowledge on these molecular interactions to enable design and development of protein-based therapeutics and vaccines. Applicants should have at least one first-author publication in
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and Hirsch, including: Weekly one-on-one meetings with faculty mentors to discuss research progress. Participation in lab meetings and journal clubs to enhance knowledge in epilepsy and neurophysiology
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to find science-based answers to critical questions concerning the risks and potential medical benefits associated with cannabis and its derivatives. We are also dedicated to translating the knowledge we
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to translating the knowledge we gain into helpful, practical applications that could enhance patient care and inform public policy. The Center supports pilot projects from Yale-affiliated researchers, aiming
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collaboratively in an interdisciplinary team. Good to have Experience in smart contract development, preferably in Solidity. Familiarity with cloud computing and shell script. Knowledge of biomedical blockchain